This paper addresses the detection of hydrogen leaks for safety warning systems in automotive applications and the measurement of nitrogen oxide concentration in exhaust gases of zero-emission vehicles. The presented approach is based on the development of accurate models (including nonlinearity and error sources of real building components) for all the system elements: sensors and acquisition chain. This methodology enables efficient design space exploration and sensitivity analysis, allowing an optimal analog-digital and hardware-software partitioning. Such analysis drives also the development of effective data fusion techniques to reduce the measure uncertainty (due to cross-sensitivity to other gases or to temperature/humidity variations). Such techniques have been implemented on a microcontroller-based mixed-signal embedded platform for intelligent sensor interfacing with limited complexity, suitable for automotive applications.